Unsupervised Machine Learning: Clustering in R and SAS

Businesses and researchers rely on unsupervised machine learning to discover hidden patterns and natural groupings in complex datasets. Understanding how to segment data without predefined labels is a critical skill for any aspiring data analyst or data scientist. This course provides a clear, conceptual foundation in cluster analysis, guiding you from basic terminology to practical implementation. You will learn how to prepare your data, select the right clustering algorithms, and write clean, reproducible code in both SAS and R to solve real-world segmentation problems. What you'll learn: - Understand the fundamental principles of unsupervised machine learning and how clustering differs from supervised classification. - Master hierarchical clustering techniques, including agglomerative methods, and learn how to interpret dendrograms. - Implement non-hierarchical K-means clustering and determine the optimal number of clusters using scree plots and modern evaluation metrics. - Prepare and standardize raw data to ensure accurate and unbiased clustering results. - Write and execute clustering programs using both SAS procedures and modern R programming workflows. - Analyze and interpret cluster outputs to extract actionable business and marketing insights. You will start with the core statistical concepts of distance and similarity before moving on to step-by-step programming walkthroughs. The material guides you through data preprocessing, algorithm execution, and the final interpretation of your clustering results. This course is designed for beginners, data enthusiasts, and junior analysts looking to expand their statistical programming toolkit, with no prior machine learning experience required. Start reading today to unlock the power of unsupervised learning and turn raw data into structured insights.